Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, how does the depth of a decision tree affects the accuracy, why does the bias of shallow decision tree is higher than that of deeper trees?
Machine Learning MCQ - Bias of shallow decision tree is greater than the bias of deeper tree
1. Consider T1, a
decision stump (tree of depth 2) and T2, a decision tree that is grown till a
maximum depth of 4. Which of the following is/are correct?
a) Bias(T1) < Bias(T2)
b) Bias(T1) > Bias(T2)
c) Variance(T1) > Variance(T2)
d) None of the above
Answer: (b) Bias(T1) > Bias(T2) A shallow (limited depth) decision tree like this (depth
2) has high bias (and low variance) because
it makes very strong assumptions about the underlying data. It assumes that
the data can be divided into very few classes, which usually too simplistic
for many real-world problems. A tree with high bias and low variance will
result in poor accuracy on the training and test data.
In simpler terms, if the tree is shallow then we are
not checking a lot of conditions/constrains i.e., the logic is simple or less
complex.
High bias
means the model consistently makes inaccurate predictions by missing
important patterns and relationships in the data, and this behavior leads to underfitting
the data.
A decision tree that grows deeper will be a complex
tree and will be overfitting with low bias (and high variance).
Model complexity
Answer C is not correct because variance of decision trees with smaller depths will be smaller than the variance of decision trees with higher depths. Refer bias variance tradeoff (link1, link2) for more information. |
Related links:
How does the depth of decision tree affect the accuracy of the model?
Decision tree with smaller depth will have high bias than the tree with low bias
Compare decision trees with high bias and low bias
bias (shallow tree) is greater than the bias (deeper tree). why?
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